Suchergebnisse

Fu Jin,1 Huan-Li Luo,1 Juan Zhou,2 Ya-Nan He,1 Xian-Feng Liu,1 Ming-Song Zhong,1 Han Yang,1 Chao Li,1 Qi-Cheng Li,1 Xia Huang,1 Xiu-Mei Tian,1 Da Qiu,1 Guang-Lei He,1 Li Yin,1 Ying Wang1 1Department of Radiation Oncology, Chongqing University Cancer Hospital, Chongqing Cancer Institute, Chongqing Cancer Hospital, Chongqing, People's Republic of China; 2Forensic Identification Center, College of Criminal Investigation, Southwest University of Political Science and Law, Chongqing, People's Republic of China Abstract: Modern radiotherapy (RT) is being enriched by big digital data and intensive technology. Multimodality image registration, intelligence-guided planning, real-time tracking, image-guided RT (IGRT), and automatic follow-up surveys are the products of the digital era. Enormous digital data are created in the process of treatment, including benefits and risks. Generally, decision making in RT tries to balance these two aspects, which is based on the archival and retrieving of data from various platforms. However, modern risk-based analysis shows that many errors that occur in radiation oncology are due to failures in workflow. These errors can lead to imbalance between benefits and risks. In addition, the exact mechanism and dose–response relationship for radiation-induced malignancy are not well understood. The cancer risk in modern RT workflow continues to be a problem. Therefore, in this review, we develop risk assessments based on our current knowledge of IGRT and provide strategies for cancer risk reduction. Artificial intelligence (AI) such as machine learning is also discussed because big data are transforming RT via AI. Keywords: cancer risk, radiotherapy, workflow, big data

AbstractTwins could play a crucial role in our understanding of genetic contributions to numerous etiologically complex disorders. In China, although adult twins are relatively rare, twins will become increasingly available due to increasing twin birth rates. Thus, child twin data will be a valuable resource to contribute to the field of child and adolescent psychopathology. The first twin database of children aged from 6 to 16 was established in Chongqing, R.P., China. In this article, we will discuss our experiences in establishing the twin database, completed in three steps — the first step being to search and identify twins, the second being to keep contact with the twins and the final being to seek cooperation with the twin families, and its future prospects. Our twin database has proven to be an efficient method for the investigation and data collection of twin children in China. The results of our present study suggest that the inclusion of twin information in the residence registration of the public security bureaus in the future may ensure a smooth run of research based on the demographic resources. We propose that school networks may be adopted as the preferred method of collection of twin records for future studies.

BACKGROUND: Very little is known about the burden and determinants of stillbirths in China. We used data from a national surveillance system for health facility births to compute a stillbirth rate representative of all facility births in China and to explore sociodemographic and obstetric factors associated with variation in the stillbirth rate. METHODS: We used data from China's National Maternal Near Miss Surveillance System between Jan 1, 2012, and Dec 31, 2014, which covers 441 hospitals in 326 urban districts and rural counties. The surveillance aimed to enumerate all maternal deaths and near misses in health facilities, and collected data prospectively for all pregnant or post-partum women admitted to the obstetric department. We restricted the analysis to births of 28 or more completed weeks of gestation or 1000 g or heavier birthweight. We examined the strength of association between sociodemographic characteristics, gestational age, and obstetric complications and stillbirths using logistic regression, taking account of the sampling strategy and clustering of births within hospitals and in cases of more than one birth per woman. FINDINGS: There were 3 956 836 births and 37 855 stillbirths, giving a stillbirth rate of 8·8 per 1000 births (95% CI 8·8-8·9). The stillbirth rate was particularly high for women younger than 15 years of age (59·9 stillbirths per 1000 births), those who had not sought antenatal care (38·3 per 1000), the unmarried (32·5 per 1000), those with no education (26·9 per 1000), or those who had had four or more births (23·2 per 1000). A high proportion (29 319 [78·2%] of 37 514) of stillbirths occurred at gestational ages of younger than 37 weeks, and about two thirds (24 787 [66·1%] of 37 514) were in women without any maternal complication at the time of birth. Of babies born at normal gestations (37-41 weeks), maternal complications substantially increased the risk of stillbirth (odds ratio comparing antepartum or intrapartum complications with no complication 3·96 [95% CI 3·66-4·29]), but only a small proportion (1638 [4·4%] of 37 514) of stillbirths fell into this group. INTERPRETATION: Our analysis of nearly 4 million births in 441 health facilities in China suggests a stillbirth rate of 8·8 per 1000 births between 2012 and 2014. Stillbirths do not feature in the Chinese Government's 5 year plans and most information systems do not include stillbirths. The Government need to start paying attention to stillbirths and invest strategically in antenatal care, particularly for the most disadvantaged women, including the very young, unmarried, and illiterate, and those at high parity. FUNDING: National Health and Family Planning Commission of the People's Republic of China, National Natural Science Foundation of China, China Medical Board, WHO, and UNICEF.

As the most abundant animals on earth, nematodes are a dominant component of the soil community. They play critical roles in regulating biogeochemical cycles and vegetation dynamics within and across landscapes and are an indicator of soil biological activity. Here, we present a comprehensive global dataset of soil nematode abundance and functional group composition. This dataset includes 6,825 georeferenced soil samples from all continents and biomes. For geospatial mapping purposes these samples are aggregated into 1,933 unique 1-km pixels, each of which is linked to 73 global environmental covariate data layers. Altogether, this dataset can help to gain insight into the spatial distribution patterns of soil nematode abundance and community composition, and the environmental drivers shaping these patterns. ; This research was supported by a grant from DOB Ecology to T.W.C., a grant from the Netherlands Organization for Scientific Research (grant 016.Veni.181.078) to S.G., grants from NSF (OPP 1115245, 1341736, 0840979) to B.J.A., by a Ramon y Cajal fellow award (RYC-2016-19939) to R.C.H., a grant from UNEP & Global Environment Facility to J.E.C., grants from NERC's Soil Security Programme to R.D.B. (NE/M017028/1) T.C. (NE/M017036/1), a grant from FAPEMIG/FAPESP/VALE S.A.(CRA-RDP-00136-10) to L.B.C., through the strategic programme UID/BIA/04050/2013 (POCI-01-0145-FEDER-007569) awarded to S.R.C., a grant from CNPq PROTAX (562346/2010-4) to J.M.d.C.C., a grant from DFG (CRC990) to V.K. and S.S., a grant from the MSHE of Russia (AAAA-A17-117112850234-5) to A.A.K., grants from the Chinese Academy of Sciences (XDB15010402) and the National Natural Science Foundation of China (41877047) to Q.L., grants from the National Natural Science Foundation of China (31330011, 31170484) to W.L., grants from NERC (NE/ M017036/1) to M.M., grants from the Spanish Ministry of Innovation (CGL2009-14686-C02-01/02, CGL2013- 43675-P) to J.A.R.M., grant from the Spanish Ministry of Innovation (RYC-2016-19939) to R.C.H., grants from NSF (DEB-0450537, DEB-1145440) to P.M., T.O.P. and K. Powers, grants from the German Academic Exchange Service (PKZ 91540366) and NAFOSTED (106.05–2017.330) to T.A.D.N., by an ARC Discovery project (DP150104199) to U.N.N., by the National Key Research and Development Program of China (2016YFC0502101) and the National Natural Science Foundation of China (31370632) to K. Pan, a ERC Research Council Advanced grant (ERC-Adv 323020 SPECIALS) to W.H.v.d.P, a grant from the Natural Environment Research Council (NERC) to D.G.W., a grant from BAPHIQ (106AS-9.5.1-BQ-B3) to J.-i.Y., a grant from the Russian Foundation for Basic Research (18-29-05076) to A.V.T. The James Hutton Institute receives financial support from the Scottish Government Rural and Environment Science and Analytical Services (RESAS) division. Investigations in Northwest Russia were carried out under state order for IB KarRC RAS and are partially supported by the Russian Foundation for Basic Research (18-34-00849).

Soil organisms are a crucial part of the terrestrial biosphere. Despite their importance for ecosystem functioning, few quantitative, spatially explicit models of the active belowground community currently exist. In particular, nematodes are the most abundant animals on Earth, filling all trophic levels in the soil food web. Here we use 6,759 georeferenced samples to generate a mechanistic understanding of the patterns of the global abundance of nematodes in the soil and the composition of their functional groups. The resulting maps show that 4.4 ± 0.64 × 1020 nematodes (with a total biomass of approximately 0.3 gigatonnes) inhabit surface soils across the world, with higher abundances in sub-Arctic regions (38% of total) than in temperate (24%) or tropical (21%) regions. Regional variations in these global trends also provide insights into local patterns of soil fertility and functioning. These high-resolution models provide the first steps towards representing soil ecological processes in global biogeochemical models and will enable the prediction of elemental cycling under current and future climate scenarios. ; This research was supported by a grant from DOB Ecology to T.W.C., a grant from the Netherlands Organization for Scientific Research (grant 016.Veni.181.078) to S.G., grants from NSF (OPP 1115245, 1341736, 0840979) to B.J.A., by a Ramon y Cajal fellow award (RYC-2016-19939) to R.C.H., a grant from UNEP & Global Environment Facility to J.E.C., a grant from NERC (NE/M017036/1) to T.C., a grant from FAPEMIG/FAPESP/VALE S.A.(CRA-RDP-00136-10) to L.B.C., through the strategic programme UID/BIA/04050/2013 (POCI-01-0145-FEDER-007569) awarded to S.R.C., a grant from CNPq PROTAX (562346/2010-4) to J.M.d.C.C., a grant from DFG (CRC990) to V.K. and S.S., a grant from the MSHE of Russia (AAAA-A17-117112850234-5) to A.A.K., grants from the Chinese Academy of Sciences (XDB15010402) and the National Natural Science Foundation of China (41877047) to Q.L., grants from the National Natural Science Foundation of China (31330011, 31170484) to W.L., grants from NERC (NE/M017036/1) to M.M., grants from the Spanish Ministry of Innovation (CGL2009-14686-C02-01/ 02, CGL2013-43675-P) to J.A.R.M., grants from NSF (DEB-0450537, DEB-1145440) to P.M., T.O.P. and K. Powers, grants from the German Academic Exchange Service (PKZ 91540366) and NAFOSTED (106.05 – 2017.330) to T.A.D.N., by an ARC Discovery project (DP150104199) to U.N.N., by the National Key Research and Development Program of China (2016YFC0502101) and the National Natural Science Foundation of China (31370632) to K. Pan, a grant from the Natural Environment Research Council (NERC) to D.G.W., a grant from BAPHIQ (106AS-9.5.1-BQ-B3) J.-i.Y. The James Hutton Institute receives financial support from the Scottish Government Rural and Environment Science and Analytical Services (RESAS) division. Investigations in northwest Russia were carried out under state order for IB KarRC RAS and are partially supported by the Russian Foundation for Basic Research (18-34-00849). We thank E. Clark and A. Orgiazzi for review of the manuscript; and R. Bouharroud, Z. Ferji, L. Jackson and E. Mzough for providing data. ; Peer reviewed